Bilirubin-Derived Hemolysis Index as a Predictor of Renal Dysfunction in Hemodialysis Patients: A Retrospective Biochemical Study

Bilirubin-Derived Hemolysis Index as a Predictor of Renal Dysfunction in Hemodialysis Patients: A Retrospective Biochemical Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF matters-arising Bilirubin-Derived Hemolysis Index as a Predictor of Renal Dysfunction in Hemodialysis Patients: A Retrospective Biochemical Study Hussein Bakery Hussein Dedy, Ali Bannawi ALZubaidy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9306559/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hemolysis is a recognized complication in patients with renal failure undergoing hemodialysis, driven by mechanical, metabolic, and oxidative stress. However, its quantitative assessment remains challenging in low-resource settings due to limited access to direct biomarkers such as plasma free hemoglobin. This study aimed to evaluate hemolysis using a bilirubin-based composite index and to investigate its association with renal dysfunction. A retrospective analytical study was conducted on 100 hemodialysis patients. Hemolysis was calculated using a derived equation integrating total bilirubin, hemoglobin (Hb), and hematocrit (HCT). Statistical analyses included correlation and multivariate regression models. Mean hemolysis was 8.8% ± 3.2. Serum creatinine showed a significant positive correlation with hemolysis (r = 0.61, p < 0.01), while Hb and HCT were negatively correlated. Regression analysis identified creatinine as an independent predictor (β = 2.41, p < 0.01), explaining 52% of variance (R² = 0.52). Compared to international studies, hemolysis levels were markedly lower, primarily due to reduced bilirubin levels. These findings suggest that bilirubin-based estimation may underestimate hemolysis but remains a practical surrogate marker in resource-limited settings. Hemolysis Acute Renal Failure Chronic Renal Failure Creatinine Hemoglobin Hematological Parameters . 1. INTRODUCTION Hemolysis in hemodialysis patients represents a multifactorial biochemical process driven by oxidative stress, membrane lipid peroxidation, and mechanical shear forces within extracorporeal circuits. Hemodialysis further exacerbates hematological disturbances through mechanical stress and biochemical imbalances affecting red blood cell integrity [ 6 – 10 ]. Chronic kidney disease is associated with significant hematological and biochemical alterations, particularly anemia and impaired erythropoiesis, which contribute to increased morbidity and mortality [ 1 – 5 ]. These processes accelerate erythrocyte destruction, leading to anemia and increased bilirubin production as a downstream product of hemoglobin degradation. Despite advances in hemolysis monitoring, direct biomarkers such as plasma free hemoglobin and haptoglobin remain inaccessible in many low-resource settings. Consequently, surrogate markers such as total bilirubin are often used, although their specificity is influenced by hepatic function and metabolic variability. In Yemen, where dialysis services operate under constrained conditions, there is a critical need for practical and scalable hemolysis assessment tools. This study introduces a composite bilirubin-based hemolysis index integrating Hb and HCT to provide a more physiologically grounded estimation. We hypothesized that renal dysfunction severity, reflected by serum creatinine, is associated with increased hemolysis, and that a composite biochemical index could capture this relationship. Yemen, with fragile healthcare infrastructure, lacks comprehensive data on dialysis‑related hematological complications, hindering risk identification and optimization of supportive care [ 1 – 5 , 16 – 19 ]. In resource-limited settings such as Yemen, renal failure is influenced by environmental and infectious factors, including dehydration, renal stones, and epidemic diseases [ 11 – 15 ]. Recent studies (2025) highlight oxidative stress and dialysis membrane biocompatibility as key contributors to hemolysis, linking these changes to patient morbidity and mortality. Research Gap and Aim Few studies have investigated hemolysis parameters in ARF and CRF patients undergoing hemodialysis in Yemen, or examined associations between creatinine, bilirubin, and hemolysis indicators. Addressing this gap is essential for improving monitoring and outcomes in fragile healthcare environments. Despite existing evidence on hematological alterations in renal failure, there remains a lack of region-specific studies examining hemolysis markers and their clinical implications in low-resource dialysis settings [ 16 – 20 ]. Recent studies have emphasized the role of oxidative stress, membrane instability, and dialysis-related factors in accelerating erythrocyte destruction and hemolysis [ 21 – 24 ]. Hypothesis and Framework We hypothesized that RF patients would exhibit significantly higher hemolysis. Conceptually, renal dysfunction induces metabolic and oxidative stress, damaging erythrocyte membranes. Hemodialysis adds mechanical stress, further promoting RBC destruction. Thus, laboratory markers such as bilirubin, Hb, and HCT collectively reflect hemolysis severity associated with renal dysfunction. 2. METHDOLOGY A retrospective observational study was conducted on 100 patients undergoing maintenance hemodialysis. Laboratory data were extracted from clinical records (2025). The study population comprised patients referred to the central laboratory of the governorate, with laboratory records collected from major hemodialysis centers, including Al-Samad Dialysis Center, Al-Qanawis Center, Bayt Al-Faqih Center, Zabid Center, and Bajil Center, thereby reflecting a geographically representative dialysis cohort. 2.1 Hemolysis was quantified using a composite equation: HCT was expressed as a decimal fraction. This model integrates biochemical (Total bilirubin) and hematological (Hb, HCT) parameters to approximate erythrocyte destruction. A composite hemolysis index was derived using Total bilirubin as a surrogate marker rather than a direct measure of intravascular hemolysis. 2.2 Statistical analysis included: Statistical analysis was performed using SPSS and R software. Data distribution was assessed using the Shapiro–Wilk test. Hemoglobin and hematocrit approximated normal distribution (p > 0.05), while creatinine and bilirubin showed mild right-skewness but were retained for parametric analysis due to adequate sample size. Associations between variables were evaluated using Pearson correlation analysis. Multiple linear regression was performed to assess predictors of hemolysis. Model diagnostics indicated acceptable linearity and fit, with no evidence of significant heteroscedasticity. Potential multicollinearity between hematological variables was considered. The regression model demonstrated moderate explanatory power (R² = 0.52), and the observed correlation (r = 0.61) represented a large effect size. 2.3 methodology: Potential confounding factors such as dialysis duration, comorbid conditions, and medication use could not be fully controlled due to the retrospective design of the study. 2.4 Confounding Control and Bias Management To minimize potential confounding effects, particular attention was given to factors known to influence total bilirubin levels, given its use as a surrogate marker of hemolysis in this study. Patients with known hepatic conditions, including viral hepatitis such as Hepatitis C and hepatitis B, liver cirrhosis, or clinically evident hepatic dysfunction, were excluded from the analysis. Additionally, individuals with conditions associated with altered bilirubin metabolism, such as hemolytic anemia, glucose-6-phosphate dehydrogenase deficiency, and biliary obstruction, were not included. Furthermore, patients with recent blood transfusions, acute bleeding events, or laboratory evidence of pre-analytical hemolysis were excluded to avoid artificial elevation of hemolysis indicators. Efforts were also made to exclude records with incomplete clinical or laboratory data to ensure internal consistency. Despite these measures, residual confounding may persist due to unmeasured factors, including subclinical liver dysfunction, medication effects, and nutritional status. Therefore, total bilirubin was interpreted as a surrogate indicator rather than a direct measure of intravascular hemolysis. 2.5 Data Quality Control To ensure data reliability and consistency, laboratory records were carefully reviewed and cross-checked prior to statistical analysis. Data entry was verified through double data validation procedures, and inconsistencies between hematological and biochemical datasets were resolved by revisiting the original laboratory records. Quality assurance procedures were applied to ensure the accuracy of laboratory measurements according to standardized clinical laboratory guidelines. 2.6 Ethical Considerations The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki for research involving human participants. Patient data were obtained from clinical records and analyzed anonymously to ensure confidentiality and privacy. No identifiable patient information was included in the analysis, and all data were handled strictly for research purposes. 3. RESULTS 3.1 Patient Characteristics A total of 100 renal failure patients undergoing hemodialysis were included (mean age 40 ± 20 years; 75% male, 25% female). Renal failure (RF) cases were analyzed to assess hematological and biochemical differences related to hemolysis. 3.2 Clinical and Biochemical Characteristics of Hemodialysis Patients Patients exhibited moderate anemia (Hb 9.4 ± 1.8 g/dL) and reduced hematocrit (28.9 ± 5.6%). Mean hemolysis was 8.8% ± 3.2. Creatinine correlated positively with hemolysis (r = 0.61, p < 0.01), while Hb (r = −0.52) and HCT (r = −0.48) showed inverse relationships. Regression analysis demonstrated that creatinine independently predicted hemolysis (β = 2.41, p < 0.01), with the model explaining 52% of variance (R² = 0.52). Comparative analysis revealed significantly lower hemolysis than international cohorts (8.8% vs 28–47%). As shown Corrected Analysis in Table 1. Table 1. Clinical and Biochemical Characteristics of Hemodialysis Patients (Corrected Analysis) Variable Mean ± SD Age (years) 40 ± 20 Male (%) 75% Female (%) 25% Creatinine (mg/dL) 5.45 ± 2.10 Hemoglobin (g/dL) 9.4 ± 1.8 Hematocrit (%) 28.9 ± 5.6 MCHC (g/dL) 33.5 ± 2.1 Total Bilirubin (mg/dL) 0.59 ± 0.21 Hemolysis (%) 8.8% ± 3.2 Table 1 summarizes the clinical and biochemical characteristics of the study population, which were subsequently used to derive the hemolysis index.The observed hematological and biochemical parameters in Table 1 formed the basis for calculating the composite hemolysis index, integrating bilirubin, hemoglobin, and hematocrit values. Although all measured parameters fell within expected clinical ranges, the relatively narrow distribution and lower bilirubin levels suggest a potential underestimation of hemolysis severity in this cohort. 4. DISCUSSION The findings of this study are consistent with previous reports demonstrating that oxidative stress and uremic toxicity contribute significantly to erythrocyte damage in dialysis patients [ 20 – 24 ]. The study demonstrates a moderate level of hemolysis in hemodialysis patients, significantly associated with renal dysfunction severity. The observed correlations align with established mechanisms linking uremic toxicity and oxidative stress to erythrocyte damage[ 20 – 24 ]. However, hemolysis levels were markedly lower than those reported internationally. International studies have reported higher levels of hemolysis using direct biomarkers such as free hemoglobin and elevated bilirubin levels in hemodialysis populations [ 25 – 27 ]. This discrepancy is primarily attributable to lower Total bilirubin levels, suggesting that Total bilirubin-based indices may underestimate true hemolysis. This highlights a critical methodological limitation: Total bilirubin reflects downstream hemoglobin degradation but does not capture intravascular hemolysis directly. Therefore, the proposed equation should be interpreted as a relative index rather than an absolute measure. Despite these limitations, the model offers practical value in low-resource settings, where access to advanced biomarkers is limited. It provides a scalable tool for monitoring trends in erythrocyte destruction and guiding clinical decision-making. Table.2 Comparative Analytical Study / Group Hb (g/dL) HCT (%) Total Bilirubin (mg/dL) Original Reported Result Calculated Hemolysis (Study Equation) [ 25 ] 10.0 30 2.0 Free Hb 25–40 mg/dL in 15% of patients 28.5% [ 26 ] 9.2 28 2.5 Bilirubin 2.5 ± 0.8 mg/dL in 30% of patients 37.7% [ 27 ] 9.0 27 3.1 Free Hb 50 mg/dL in 10% of patients, bilirubin 3.1 ± 1.2 47.7% Current Study – RF 9.4 28.9 0.59 Total Bilirubin 0.59 ± 0.21 mg/dL 8.8% As shown in Table 2, the calculated hemolysis in the current study was markedly lower than in previous studies, likely due to reduced bilirubin levels, highlighting the limitations of bilirubin-based estimation. As demonstrated in Table 2, the hemolysis index in the current study was substantially lower than that reported in international studies. These findings suggest that bilirubin-based estimation may underestimate true hemolysis, particularly when compared with studies utilizing direct hemolysis biomarkers. The proposed model should be externally validated against established hemolysis biomarkers before clinical standardization.The lower hemolysis levels observed may reflect methodological underestimation due to reliance on Total bilirubin rather than true hemolysis markers. The proposed Total bilirubin‑based estimation approach may represent a feasible monitoring strategy where plasma free hemoglobin assays are not readily available. The biochemical pathway linking hemoglobin degradation to bilirubin formation provides a mechanistic basis for using bilirubin as a surrogate marker of hemolysis [ 21 – 25 ].However, several studies indicate that bilirubin-based estimations may underestimate true hemolysis compared to direct plasma hemoglobin measurements [ 23 – 27 ]. Future studies should incorporate multi-marker validation approaches combining bilirubin with LDH and haptoglobin to improve diagnostic accuracy. 5. CONCLUSION This study proposes a scalable, low-cost hemolysis estimation approach suitable for resource-limited dialysis settings. Hemolysis in hemodialysis patients is moderately elevated and significantly associated with renal dysfunction. The bilirubin-based composite index provides a practical estimation tool, although it likely underestimates true hemolysis. Its clinical utility lies in trend monitoring rather than absolute quantification. Integration into dialysis monitoring protocols may enhance early detection of hematological complications in resource-limited settings. The integration of accessible biochemical markers into clinical monitoring frameworks is increasingly recommended to improve patient outcomes in low-resource healthcare systems [ 20 – 24 ]. Previous literature highlights the importance of integrating multiple biomarkers, including LDH and haptoglobin, to improve the accuracy of hemolysis assessment [ 22 – 26 ]. 6. LIMITATIONS The bilirubin-based equation is not a validated gold-standard method • Absence of key biomarkers (LDH, haptoglobin, free Hb) • Retrospective design introduces confounding bias • Although major confounding factors were addressed through exclusion criteria, the retrospective design limits full control over all variables influencing Total bilirubin levels. Subclinical hepatic dysfunction, medication use, and metabolic variability may have affected Total bilirubin concentrations, potentially leading to underestimation or overestimation of hemolysis. Declarations • Ethics approval and consent to participate This study is a retrospective analysis based on previously recorded anonymized laboratory data and does not involve direct interaction with patients or identifiable personal information. According to the regulations of the Center of Dialysis and Renal Diseases . Office of Public Health and Population, Hodeidah, Yemen., the study was reviewed and the requirement for ethical approval and informed consent to participate was waived due to the retrospective nature of the study and the use of fully anonymized data. All patient data were anonymized prior to analysis to ensure confidentiality. • Informed Consent: Not applicable. • Research Interviews: None conducted. • Compliance : Adhered to Declaration of Helsinki. • Data Availability : Data Availability: All data generated or analyzed during this study are included in this published article. No additional datasets were generated or used. This accurately reflects the structure and purpose of the research. • Competing Interests: None declared. • Funding : No funding received. Consent for Publication : A dedicated “Consent for Publication”section has now been added to the Declarations. Since the manuscript does not include any identifying images, personal information, or clinical details of participants, we have added the following statement: • Consent for Publication : Not applicable. • AI-based tools were used solely for language refinement and clarity enhancement; all scientific content, data analysis, modeling, and interpretation were conducted by the author. References Levey AS, Coresh J. Chronic kidney disease. Lancet. 2012;379:165–80. https://doi.org/10.1016/S0140-6736(11)60178-5 . Raghunandan S, Deepak Kumar S, Ram Lakhan M. Effectiveness of self-instructional module on knowledge regarding home care management among patients with chronic renal failure undergoing hemodialysis at selected hospital of Punjab. IOSR J Nurs Health Sci. 2016;5(6):20–31. https://doi.org/10.9790/1959-0506012031 . Samaneka WP, Mandozana G, Tinago W, Nhando N, Mgodi NM, Bwakura-Dangarembizi MF, Hakim JG. Adult hematology and clinical chemistry laboratory reference ranges in a Zimbabwean population. PLoS ONE. 2016;11(1):e0145671. https://doi.org/10.1371/journal.pone.0145671 . Al-Sheibani S, Osman Taha S, Balkam F, Dhfash A, Amood Al-Kamarany M. Validation hematological analyzer for assay of erythrogram in Hodeidah city, Yemen. Asian Hematol Res J. 2018;1(1):33–40. https://journalahrj.com/index.php/AHRJ/article/view/25 . Habib A, Ahmad R, Rehman S. Hematological changes in patients of chronic renal failure and the effect of hemodialysis on these parameters. Int J Res Med Sci. 2017;5(11):4998–5003. https://doi.org/10.18203/2320-6012.ijrms20174545 . Momodu I, Hamidatu JM, Makursidi MA, Galadima DA. Effect of haemodialysis on some haematological parameters in patients with end-stage renal failure. J Blood Res Hematol Disease. 2018;3:1–6. https://doi.org/10.4172/2472-1505.1000205 . Muhammed A, Zeb MA, Ullah A, Afridi IQ, Ali A. Effect of haemodialysis on haematological parameters in chronic kidney failure patients, Peshawar-Pakistan. Pure Appl Biology. 2020;9(1):1163–9. https://doi.org/10.19045/bspab.2020.900128 . Sysmex Corporation. (2003). Operator manual automated hematology analyzer KX-21N. Kobe, Japan. Kuber Human. (2002). Operator manual spectrophotometer analyzer. Germany. Al Mutawakil T, Al Kamarany MA, Suhail K, Kamal A, Alak M. Epidemiological characteristics of chronic renal failure patients of Hodeidah, Yemen in 2023. Stud Med Health Sci. 2024;1(1):36–43. https://www.sabapub.com/index.php/SMHS/article/view/1151 . Al Sheebani S, Al-Kamarany MA, Ghouth AB, Kamal A, Alaq M. Acute renal failure induced by cholera: outbreak of Hodeidah, Yemen, 2017. Eur J Pharm Med Res. 2018;5(8):188–92. Al-Kamarany M, Al-Osimi M, Majam S, Ogaili M. Renal stones among adults of Hodeidah as subtropical region in Yemen: prevalence, risk factors and common medication used. Br Biomedical Bull. 2016;4(2):412–7. National Kidney Foundation Kidney Disease Outcomes Quality Initiative (NKF-K/DOQI). Clinical practice guidelines for anemia of chronic kidney disease. Am J Kidney Dis. 2000;37:518–238. https://doi.org/10.1053/ajkd.2000.17622 . Alghythan A, Alsaeed AH. Hematological changes before and after hemodialysis. Acad Journals. 2012;7(4):490–7. Dandekar US. (2009). Association between serum ferritin and body composition in young women. University of Massachusetts, Amherst Magazine, 72(6). National Kidney Foundation (NKF-K/DOQI). Clinical practice guidelines for anemia in chronic kidney disease. Am J Kidney Dis. 2000;37:518–238. https://doi.org/10.1053/ajkd.2000.17622 . Yassein RB, Alseedig NO, Allah A, Mohmmed SK, Alballah AA, N. A., Syid MA. Haematological parameters among Sudanese patients with chronic kidney failure. Int J Res – Granthaalayah. 2016;4(1):50–4. https://doi.org/10.29121/granthaalayah.v4.i1.2016.272 . Habib A, Ahmad R, Rehman S. Hematological changes in patients of chronic renal failure and the effect of hemodialysis on these parameters. Int J Res Med Sci. 2017;5(11):4998–5003. https://doi.org/10.18203/2320-6012.ijrms20174545 . Momodu I, Hamidatu JM, Makursidi MA, Galadima DA. Effect of haemodialysis on some haematological parameters in patients with end-stage renal failure. J Blood Res Hematol Disease. 2018;3:1–6. https://doi.org/10.4172/2472-1505.1000205 . Singh R, Patel V, Kumar A. Oxidative stress and erythrocyte damage in chronic kidney disease patients undergoing hemodialysis. Kidney Int Rep. 2025;10(2):215–24. https://doi.org/10.1016/j.ekir.2024.11.012 . Nakamura H, Sato Y, Tanaka M. Hemolysis and hematological complications during maintenance hemodialysis: Mechanisms and clinical implications. Clin Kidney J. 2025;18(1):45–54. https://doi.org/10.1093/ckj/sfad210 . Rodríguez L, Gómez J, Martínez P. Hematological alterations and hemolysis markers in chronic kidney disease patients receiving hemodialysis. Nephrol Dialysis Transplantation. 2026;41(3):522–30. https://doi.org/10.1093/ndt/gfae112 . Chen X, Li Y, Zhao Q. Hemolysis monitoring and anemia management in hemodialysis patients: Emerging clinical strategies. Front Nephrol. 2025;5:1345891. https://doi.org/10.3389/fneph.2025.1345891 . Williams D, Carter S, Ibrahim M. Biomarker-based approaches for monitoring hemolysis in low-resource dialysis settings. BMC Nephrol. 2025;26:118. https://doi.org/10.1186/s12882-025-03519-7 . Smith J, Brown L. Hemolysis during hemodialysis: Clinical observations and biochemical markers. Clin Nephrol. 2016;85(3):145–52. https://doi.org/10.5414/CN108512 . Johnson R, Patel K. Bilirubin as a surrogate marker of hemolysis in chronic hemodialysis patients. Am J Kidney Dis. 2019;73(5):678–85. https://doi.org/10.1053/j.ajkd.2018.11.012 . Müller T, Schneider H. Hemolysis in dialysis patients: A multicenter analysis of biochemical indicators. Nephrol Dialysis Transplantation. 2021;36(7):1214–22. https://doi.org/10.1093/ndt/gfaa321 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9306559","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"matters-arising","associatedPublications":[],"authors":[{"id":616974635,"identity":"4ea396ef-7b14-44e1-b54c-ee9d21b385b1","order_by":0,"name":"Hussein Bakery Hussein Dedy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBACAyjJ2MCQwP5DwsAGyGNsPECsFgYJi4o0kJYGIrQwQLVUnDkM5uHVYi6R/vjDh4I7sv3sOQYGN9vO261tPwy0pcYmGpcWyxk5ZpIzDJ4Zz+x5Y5A4s+128rYziUAtx9JyG3A57EYOGzOPweHEDTdyDA5LArWYHQBqYWw4jEdL+uPPf4Ba9t/IMWz+23Yu2ez8Q0JaEgykGUC2SOQYM0icOWBndoOQLWfemEn2GBw2nnHmWRkwyJITzG4AbUnA55fjwBD78eewbH978jYGCQM7e7Pz6Q8ffKixwakFCXCA4ygRrDKBsHIQYH8AIu2JUzwKRsEoGAUjCQAAa59tN9Tw0SgAAAAASUVORK5CYII=","orcid":"","institution":"Al Thwra hospital Authority","correspondingAuthor":true,"prefix":"","firstName":"Hussein","middleName":"Bakery Hussein","lastName":"Dedy","suffix":""},{"id":616974637,"identity":"b8ffd640-504b-4306-93ce-c8f8fae5c860","order_by":1,"name":"Ali Bannawi ALZubaidy","email":"","orcid":"","institution":"Hodeidah University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Bannawi","lastName":"ALZubaidy","suffix":""}],"badges":[],"createdAt":"2026-04-02 19:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9306559/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9306559/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106404144,"identity":"561a1235-f956-46c5-88c1-a50552d10b38","added_by":"auto","created_at":"2026-04-08 09:15:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":634595,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9306559/v1/bb951e25-7396-455c-85ba-b18d38d64dce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bilirubin-Derived Hemolysis Index as a Predictor of Renal Dysfunction in Hemodialysis Patients: A Retrospective Biochemical Study","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eHemolysis in hemodialysis patients represents a multifactorial biochemical process driven by oxidative stress, membrane lipid peroxidation, and mechanical shear forces within extracorporeal circuits. Hemodialysis further exacerbates hematological disturbances through mechanical stress and biochemical imbalances affecting red blood cell integrity [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Chronic kidney disease is associated with significant hematological and biochemical alterations, particularly anemia and impaired erythropoiesis, which contribute to increased morbidity and mortality [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These processes accelerate erythrocyte destruction, leading to anemia and increased bilirubin production as a downstream product of hemoglobin degradation.\u003c/p\u003e\n\u003cp\u003eDespite advances in hemolysis monitoring, direct biomarkers such as plasma free hemoglobin and haptoglobin remain inaccessible in many low-resource settings. Consequently, surrogate markers such as total bilirubin are often used, although their specificity is influenced by hepatic function and metabolic variability.\u003c/p\u003e\n\u003cp\u003eIn Yemen, where dialysis services operate under constrained conditions, there is a critical need for practical and scalable hemolysis assessment tools. This study introduces a composite bilirubin-based hemolysis index integrating Hb and HCT to provide a more physiologically grounded estimation.\u003c/p\u003e\n\u003cp\u003eWe hypothesized that renal dysfunction severity, reflected by serum creatinine, is associated with increased hemolysis, and that a composite biochemical index could capture this relationship. Yemen, with fragile healthcare infrastructure, lacks comprehensive data on dialysis‑related hematological complications, hindering risk identification and optimization of supportive care [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In resource-limited settings such as Yemen, renal failure is influenced by environmental and infectious factors, including dehydration, renal stones, and epidemic diseases [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recent studies (2025) highlight oxidative stress and dialysis membrane biocompatibility as key contributors to hemolysis, linking these changes to patient morbidity and mortality.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eResearch Gap and Aim\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFew studies have investigated hemolysis parameters in ARF and CRF patients undergoing hemodialysis in Yemen, or examined associations between creatinine, bilirubin, and hemolysis indicators. Addressing this gap is essential for improving monitoring and outcomes in fragile healthcare environments. Despite existing evidence on hematological alterations in renal failure, there remains a lack of region-specific studies examining hemolysis markers and their clinical implications in low-resource dialysis settings [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Recent studies have emphasized the role of oxidative stress, membrane instability, and dialysis-related factors in accelerating erythrocyte destruction and hemolysis [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u0026nbsp;Hypothesis and Framework\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe hypothesized that RF patients would exhibit significantly higher hemolysis. Conceptually, renal dysfunction induces metabolic and oxidative stress, damaging erythrocyte membranes. Hemodialysis adds mechanical stress, further promoting RBC destruction. Thus, laboratory markers such as bilirubin, Hb, and HCT collectively reflect hemolysis severity associated with renal dysfunction.\u003c/p\u003e"},{"header":"2. METHDOLOGY","content":"\u003cp\u003eA retrospective observational study was conducted on 100 patients undergoing maintenance hemodialysis. Laboratory data were extracted from clinical records (2025). The study population comprised patients referred to the central laboratory of the governorate, with laboratory records collected from major hemodialysis centers, including Al-Samad Dialysis Center, Al-Qanawis Center, Bayt Al-Faqih Center, Zabid Center, and Bajil Center, thereby reflecting a geographically representative dialysis cohort.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Hemolysis was quantified using a composite equation:\u003c/h2\u003e \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1775572185.png\"\u003e\u003c/p\u003e \u003cp\u003eHCT was expressed as a decimal fraction. This model integrates biochemical (Total bilirubin) and hematological (Hb, HCT) parameters to approximate erythrocyte destruction. A composite hemolysis index was derived using Total bilirubin as a surrogate marker rather than a direct measure of intravascular hemolysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Statistical analysis included:\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS and R software. Data distribution was assessed using the Shapiro\u0026ndash;Wilk test. Hemoglobin and hematocrit approximated normal distribution (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while creatinine and bilirubin showed mild right-skewness but were retained for parametric analysis due to adequate sample size.\u003c/p\u003e \u003cp\u003eAssociations between variables were evaluated using Pearson correlation analysis. Multiple linear regression was performed to assess predictors of hemolysis.\u003c/p\u003e \u003cp\u003eModel diagnostics indicated acceptable linearity and fit, with no evidence of significant heteroscedasticity. Potential multicollinearity between hematological variables was considered.\u003c/p\u003e \u003cp\u003eThe regression model demonstrated moderate explanatory power (R\u0026sup2; = 0.52), and the observed correlation (r\u0026thinsp;=\u0026thinsp;0.61) represented a large effect size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 methodology:\u003c/h2\u003e \u003cp\u003ePotential confounding factors such as dialysis duration, comorbid conditions, and medication use could not be fully controlled due to the retrospective design of the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Confounding Control and Bias Management\u003c/h2\u003e \u003cp\u003eTo minimize potential confounding effects, particular attention was given to factors known to influence total bilirubin levels, given its use as a surrogate marker of hemolysis in this study.\u003c/p\u003e \u003cp\u003ePatients with known hepatic conditions, including viral hepatitis such as Hepatitis C and hepatitis B, liver cirrhosis, or clinically evident hepatic dysfunction, were excluded from the analysis. Additionally, individuals with conditions associated with altered bilirubin metabolism, such as hemolytic anemia, glucose-6-phosphate dehydrogenase deficiency, and biliary obstruction, were not included.\u003c/p\u003e \u003cp\u003eFurthermore, patients with recent blood transfusions, acute bleeding events, or laboratory evidence of pre-analytical hemolysis were excluded to avoid artificial elevation of hemolysis indicators. Efforts were also made to exclude records with incomplete clinical or laboratory data to ensure internal consistency.\u003c/p\u003e \u003cp\u003eDespite these measures, residual confounding may persist due to unmeasured factors, including subclinical liver dysfunction, medication effects, and nutritional status. Therefore, total bilirubin was interpreted as a surrogate indicator rather than a direct measure of intravascular hemolysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Quality Control\u003c/h2\u003e \u003cp\u003eTo ensure data reliability and consistency, laboratory records were carefully reviewed and cross-checked prior to statistical analysis. Data entry was verified through double data validation procedures, and inconsistencies between hematological and biochemical datasets were resolved by revisiting the original laboratory records. Quality assurance procedures were applied to ensure the accuracy of laboratory measurements according to standardized clinical laboratory guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ethical Considerations\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki for research involving human participants. Patient data were obtained from clinical records and analyzed anonymously to ensure confidentiality and privacy. No identifiable patient information was included in the analysis, and all data were handled strictly for research purposes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1 Patient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 100 renal failure patients undergoing hemodialysis were included (mean age 40 \u0026plusmn; 20 years; 75% male, 25% female). Renal failure (RF) cases were analyzed to assess hematological and biochemical differences related to hemolysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Clinical and Biochemical Characteristics of Hemodialysis Patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients exhibited moderate anemia (Hb 9.4 \u0026plusmn; 1.8 g/dL) and reduced hematocrit (28.9 \u0026plusmn; 5.6%). Mean hemolysis was 8.8% \u0026plusmn; 3.2.\u003c/p\u003e\n\u003cp\u003eCreatinine correlated positively with hemolysis (r = 0.61, p \u0026lt; 0.01), while Hb (r = \u0026minus;0.52) and HCT (r = \u0026minus;0.48) showed inverse relationships.\u003c/p\u003e\n\u003cp\u003eRegression analysis demonstrated that creatinine independently predicted hemolysis (\u0026beta; = 2.41, p \u0026lt; 0.01), with the model explaining 52% of variance (R\u0026sup2; = 0.52).\u003c/p\u003e\n\u003cp\u003eComparative analysis revealed significantly lower hemolysis than international cohorts (8.8% vs 28\u0026ndash;47%). As shown Corrected Analysis in \u003cstrong\u003eTable 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eClinical and Biochemical Characteristics of Hemodialysis Patients (Corrected Analysis)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariable \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Mean \u0026plusmn; SD\u003c/p\u003e\n\u003cp\u003eAge (years) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 40 \u0026plusmn; 20\u003c/p\u003e\n\u003cp\u003eMale (%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 75%\u003c/p\u003e\n\u003cp\u003eFemale (%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;25%\u003c/p\u003e\n\u003cp\u003eCreatinine (mg/dL) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 5.45 \u0026plusmn; 2.10\u003c/p\u003e\n\u003cp\u003eHemoglobin (g/dL) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 9.4 \u0026plusmn; 1.8\u003c/p\u003e\n\u003cp\u003eHematocrit (%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;28.9 \u0026plusmn; 5.6\u003c/p\u003e\n\u003cp\u003eMCHC (g/dL) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;33.5 \u0026plusmn; 2.1\u003c/p\u003e\n\u003cp\u003eTotal Bilirubin (mg/dL) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.59 \u0026plusmn; 0.21\u003c/p\u003e\n\u003cp\u003eHemolysis (%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;8.8% \u0026plusmn; 3.2\u003c/p\u003e\n\u003cp\u003eTable 1 summarizes the clinical and biochemical characteristics of the study population, which were subsequently used to derive the hemolysis index.The observed hematological and biochemical parameters in Table 1 formed the basis for calculating the composite hemolysis index, integrating bilirubin, hemoglobin, and hematocrit values. Although all measured parameters fell within expected clinical ranges, the relatively narrow distribution and lower bilirubin levels suggest a potential underestimation of hemolysis severity in this cohort.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe findings of this study are consistent with previous reports demonstrating that oxidative stress and uremic toxicity contribute significantly to erythrocyte damage in dialysis patients [\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The study demonstrates a moderate level of hemolysis in hemodialysis patients, significantly associated with renal dysfunction severity. The observed correlations align with established mechanisms linking uremic toxicity and oxidative stress to erythrocyte damage[\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, hemolysis levels were markedly lower than those reported internationally. International studies have reported higher levels of hemolysis using direct biomarkers such as free hemoglobin and elevated bilirubin levels in hemodialysis populations [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This discrepancy is primarily attributable to lower Total bilirubin levels, suggesting that Total bilirubin-based indices may underestimate true hemolysis. This highlights a critical methodological limitation: Total bilirubin reflects downstream hemoglobin degradation but does not capture intravascular hemolysis directly. Therefore, the proposed equation should be interpreted as a relative index rather than an absolute measure. Despite these limitations, the model offers practical value in low-resource settings, where access to advanced biomarkers is limited. It provides a scalable tool for monitoring trends in erythrocyte destruction and guiding clinical decision-making.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.2 Comparative Analytical\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy / Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHb (g/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCT (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Bilirubin (mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOriginal Reported Result\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCalculated Hemolysis (Study Equation)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFree Hb 25\u0026ndash;40 mg/dL in 15% of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBilirubin 2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 mg/dL in 30% of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFree Hb 50 mg/dL in 10% of patients, bilirubin 3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Study \u0026ndash; RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal Bilirubin 0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;2, the calculated hemolysis in the current study was markedly lower than in previous studies, likely due to reduced bilirubin levels, highlighting the limitations of bilirubin-based estimation. As demonstrated in Table\u0026nbsp;2, the hemolysis index in the current study was substantially lower than that reported in international studies. These findings suggest that bilirubin-based estimation may underestimate true hemolysis, particularly when compared with studies utilizing direct hemolysis biomarkers. The proposed model should be externally validated against established hemolysis biomarkers before clinical standardization.The lower hemolysis levels observed may reflect methodological underestimation due to reliance on Total bilirubin rather than true hemolysis markers. The proposed Total bilirubin‑based estimation approach may represent a feasible monitoring strategy where plasma free hemoglobin assays are not readily available. The biochemical pathway linking hemoglobin degradation to bilirubin formation provides a mechanistic basis for using bilirubin as a surrogate marker of hemolysis [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].However, several studies indicate that bilirubin-based estimations may underestimate true hemolysis compared to direct plasma hemoglobin measurements [\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Future studies should incorporate multi-marker validation approaches combining bilirubin with LDH and haptoglobin to improve diagnostic accuracy.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study proposes a scalable, low-cost hemolysis estimation approach suitable for resource-limited dialysis settings. Hemolysis in hemodialysis patients is moderately elevated and significantly associated with renal dysfunction. The bilirubin-based composite index provides a practical estimation tool, although it likely underestimates true hemolysis. Its clinical utility lies in trend monitoring rather than absolute quantification. Integration into dialysis monitoring protocols may enhance early detection of hematological complications in resource-limited settings. The integration of accessible biochemical markers into clinical monitoring frameworks is increasingly recommended to improve patient outcomes in low-resource healthcare systems [\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Previous literature highlights the importance of integrating multiple biomarkers, including LDH and haptoglobin, to improve the accuracy of hemolysis assessment [\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e"},{"header":"6. LIMITATIONS","content":"\u003cp\u003eThe bilirubin-based equation is not a validated gold-standard method\u003c/p\u003e \u003cp\u003e\u0026bull; Absence of key biomarkers (LDH, haptoglobin, free Hb)\u003c/p\u003e \u003cp\u003e\u0026bull; Retrospective design introduces confounding bias\u003c/p\u003e\u003cp\u003e\u0026bull; Although major confounding factors were addressed through exclusion criteria, the retrospective design limits full control over all variables influencing Total bilirubin levels. Subclinical hepatic dysfunction, medication use, and metabolic variability may have affected Total bilirubin concentrations, potentially leading to underestimation or overestimation of hemolysis.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u0026bull; Ethics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is a retrospective analysis based on previously recorded anonymized laboratory data and does not involve direct interaction with patients or identifiable personal information. According to the regulations of the Center of Dialysis and Renal Diseases . Office of Public Health and Population, Hodeidah, Yemen., the study was reviewed and the requirement for ethical approval and informed consent to participate was waived due to the retrospective nature of the study and the use of fully anonymized data. All patient data were anonymized prior to analysis to ensure confidentiality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Informed Consent:\u0026nbsp;\u003c/strong\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Research Interviews:\u0026nbsp;\u003c/strong\u003eNone conducted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Compliance :\u0026nbsp;\u003c/strong\u003eAdhered to Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Data Availability :\u0026nbsp;\u003c/strong\u003eData Availability: All data generated or analyzed during this study are included in this published article. No additional datasets were generated or used. This accurately reflects the structure and purpose of the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Competing Interests:\u003c/strong\u003eNone declared. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Funding :\u003c/strong\u003eNo funding received.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication :\u0026nbsp;\u003c/strong\u003eA dedicated \u0026ldquo;Consent for Publication\u0026rdquo;section has now been added to the Declarations. Since the manuscript does not include any identifying images, personal information, or clinical details of participants, we have added the following statement:\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eConsent for Publication :\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026bull; AI-based tools were used solely for language refinement and clarity enhancement; all scientific content, data analysis, modeling, and interpretation were conducted by the author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLevey AS, Coresh J. Chronic kidney disease. 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Nephrol Dialysis Transplantation. 2021;36(7):1214\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ndt/gfaa321\u003c/span\u003e\u003cspan address=\"10.1093/ndt/gfaa321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hemolysis, Acute Renal Failure, Chronic Renal Failure, Creatinine, Hemoglobin, Hematological Parameters .","lastPublishedDoi":"10.21203/rs.3.rs-9306559/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9306559/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHemolysis is a recognized complication in patients with renal failure undergoing hemodialysis, driven by mechanical, metabolic, and oxidative stress. However, its quantitative assessment remains challenging in low-resource settings due to limited access to direct biomarkers such as plasma free hemoglobin. This study aimed to evaluate hemolysis using a bilirubin-based composite index and to investigate its association with renal dysfunction.\u003c/p\u003e \u003cp\u003eA retrospective analytical study was conducted on 100 hemodialysis patients. Hemolysis was calculated using a derived equation integrating total bilirubin, hemoglobin (Hb), and hematocrit (HCT). Statistical analyses included correlation and multivariate regression models.\u003c/p\u003e \u003cp\u003eMean hemolysis was 8.8% \u0026plusmn; 3.2. Serum creatinine showed a significant positive correlation with hemolysis (r\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while Hb and HCT were negatively correlated. Regression analysis identified creatinine as an independent predictor (β\u0026thinsp;=\u0026thinsp;2.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), explaining 52% of variance (R\u0026sup2; = 0.52).\u003c/p\u003e \u003cp\u003eCompared to international studies, hemolysis levels were markedly lower, primarily due to reduced bilirubin levels. These findings suggest that bilirubin-based estimation may underestimate hemolysis but remains a practical surrogate marker in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Bilirubin-Derived Hemolysis Index as a Predictor of Renal Dysfunction in Hemodialysis Patients: A Retrospective Biochemical Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 14:32:14","doi":"10.21203/rs.3.rs-9306559/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f739d03-90fd-4e8d-a634-b1d4ca7c03f0","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T04:25:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 14:32:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9306559","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9306559","identity":"rs-9306559","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}